Two views of belief: belief as generalized probability and belief as evidence
Artificial Intelligence
The use of ARIMA models for reliability forecasting and analysis
Proceedings of the 23rd international conference on on Computers and industrial engineering
On the neural network approach in software reliability modeling
Journal of Systems and Software
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A Bayesian Belief Network for Reliability Assessment
SAFECOMP '01 Proceedings of the 20th International Conference on Computer Safety, Reliability and Security
A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Modeling Software Reliability Growth with Genetic Programming
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
Software reliability forecasting by support vector machines with simulated annealing algorithms
Journal of Systems and Software
Evolutionary neural network modeling for forecasting the field failure data of repairable systems
Expert Systems with Applications: An International Journal
Evidential reasoning approach for bridge condition assessment
Expert Systems with Applications: An International Journal
Predicting software reliability with neural network ensembles
Expert Systems with Applications: An International Journal
Software reliability identification using functional networks: A comparative study
Expert Systems with Applications: An International Journal
Software reliability models with time-dependent hazard function based on Bayesian approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Bayesian predictive software reliability model with pseudo-failures
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Belief rule-base inference methodology using the evidential reasoning Approach-RIMER
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optimization Models for Training Belief-Rule-Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
System reliability forecasting by support vector machines with genetic algorithms
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
On the dynamic evidential reasoning algorithm for fault prediction
Expert Systems with Applications: An International Journal
Assessment of E-Commerce security using AHP and evidential reasoning
Expert Systems with Applications: An International Journal
Long-term potential performance degradation analysis method based on dynamical probability model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A belief-rule-based inventory control method under nonstationary and uncertain demand
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
In this paper, a novel reliability prediction technique based on the evidential reasoning (ER) algorithm is developed and applied to forecast reliability in turbocharger engine systems. The focus of this study is to examine the feasibility and validity of the ER algorithm in systems reliability prediction by comparing it with some existing approaches. To determine the parameters of the proposed model accurately, some nonlinear optimization models are investigated to search for the optimal parameters of forecasting model by minimizing the mean square error (MSE) criterion. Finally, a numerical example is provided to demonstrate the detailed implementation procedures. The experimental results show that the prediction performance of the ER-based prediction model outperforms several existing methods in terms of prediction accuracy or speed.